Suivi spatio-temporel du couvert nival du Québec à l’aide des données NOAA-AVHRR

L’imagerie satellitaire dans le visible et l’infrarouge permet de cartographier le couvert nival à grande échelle, ce qui n’est pas facilement réalisable à partir des observations locales conventionnelles. Cependant, en raison de leur résolution spatiale inadéquate ou de la faible durée de leurs séries d’observations, les produits satellitaires actuellement disponibles sont inutilisables pour l’étude à long terme du couvert nival. Par conséquent, l’objectif de la présente étude a été de développer un algorithme opérationnel de cartographie de la neige à l’aide des données du capteur AVHRR (Advanced Very High Resolution Radiometer) embarqué à bord du satellite NOAA. Cette procédure doit permettre de suivre l’évolution spatio-temporelle de la neige au sol sur une longue période de temps et avec une bonne résolution spatiale. Les résultats de la cartographie ont été validés par rapport aux observations de l’occurrence et de l’épaisseur de la neige au sol. L’algorithme a été appliqué au territoire du Québec sur trois périodes spécifiques : 1998-1999, 1991-1992 et 1986-1987. L’algorithme a réussi à identifier la catégorie de surface (neige/non-neige) avec un taux de succès global moyen de 87 %. Les performances de l’algorithme ont été supérieures dans la détection de la neige (90 %) qu’elles l’ont été pour les surfaces sans neige (82 %). Également, l’algorithme a permis de situer le début des périodes de formation et de fonte de la neige, et ce tant au niveau local qu’à l’échelle du bassin versant.This work is part of a multidisciplinary study designed to validate the elements of the hydrological cycle of the Canadian regional climate model simulations (CRCM) over Quebec (Canada). These simulations, carried out over a 20-year period (1979-1999), aim at examining the annual and inter-annual hydrological budgets of a dozen catchments. Snow cover is a key factor in the modeling of the hydrological budget as well as the climatic changes. The remote sensing component of the project involves the use of satellite data in order to validate CRCM simulations of snow cover characteristics (i.e., snow cover extent), which are impossible to validate using conventional in situ snow observations.Satellite data in the visible and infrared spectra as well as passive microwaves represent an alternative source of information on snow cover. Various satellite snow products have been available since the middle of the 1960’s and a few are available in real time and online. However, their quality varies considerably with respect to sensor and platform characteristics, image processing procedures and snow classification techniques. Consequently, these operational products cannot be used for the validation of the CRCM simulations because of their limited spatial extent, or their coarse spatial resolution, or the lack of a continuous and homogeneous series of observations covering the targeted period (1979-1999). In addition, the coarse temporal resolution and the small areal coverage of high-resolution satellites limit their use for the temporal monitoring of snow cover on a regional scale. Consequently, it was decided to explore the potential of NOAA-AVHRR data for the space-time monitoring of snow on the ground and to produce snow cover maps. These maps would then be used to validate CRCM simulations. Among the 20 years concerned by the study (1979-1999), six winter seasons were targeted to be used in the validation process.The objective of this work was thus to develop a simple procedure of space-time monitoring of snow cover over the province of Quebec using AVHRR images. The algorithm was calibrated and validated over three winter seasons: 1998-1999, 1991-1992 and 1986-1987. In order to monitor snow cover, especially during snow setting and melt phases, the daily images from October 1st to December 15th and from April 1st to May 31st of each of the three periods were used. Images at the beginning of the afternoon were preferred since they are less sensitive to topographic effects and variation in illumination conditions. Only the images presenting a minimal cloud cover were retained (164 images out of the 411 initially identified). These selected images were used for the calibration and validation of the snow cover mapping algorithm. Selected AVHRR images were calibrated and corrected radiometrically and geometrically. A sub-region (82°30’ W, 58°N; 60° W, 46° N) covering the territory being studied was therefore extracted from each image.The classification algorithm used herein was developed from published classification techniques. This algorithm is based on sequential hierarchical thresholds in order to classify the AVHRR images into three surface categories: snow, no-snow and clouds. It consists of a combination of six sequential thresholds. The thresholds go from least restrictive to most severe. A pixel that successfully passes through all the thresholds is classified as snow; if the pixel does not pass through all the thresholds, it is categorized either as clouds or no-snow. The thresholds were established empirically and are consequently specific to Quebec conditions. The classification results were validated at the temporal and spatial levels using ground observations, specifically snow occurrence at Environment Canada’s meteorological stations.The algorithm was calibrated using pixel samples extracted from each selected image, above areas representing the three surface categories present within the scene. These areas were identified visually and delimited manually. Thereafter, radiometric data samples from all selected images were put together and their percentiles were calculated. The percentiles were used to build the values of the algorithm thresholds.For each of the three studied periods, two dates were chosen for the spatial validation of the snow maps produced using AVHRR images: one during the snow cover setting period (at the end of October) and the other for the snow melt period in spring (at the end of April). For these six dates, ground snow occurrence at meteorological stations was compared to the classification results. For temporal validation, snow occurrence observations at 15 meteorological stations during each of the three winter seasons were used for the classification algorithm. Corresponding ground observations were compared to the occurrence of snow class within 3 x 3-pixel windows centered on each station and the total accuracy statistics were therefore calculated. When 50% or more of the 3 x 3-pixel windows were classified as cloudy, the results for the corresponding station were excluded from the comparison.The classification results were quite accurate, with 87% of the pixels around validation meteorological stations being correctly identified. The algorithm successfully detected the presence of snow with a precision of 90% and 82% for no-snow surfaces. The algorithm performances in spring and autumn were similar. Also, the algorithm detected the presence of snow more accurately in open lands than in forested areas. We demonstrated that the algorithm allowed the location of the beginning of snow formation and melting periods at the local level as well as at the watershed scale, especially under clear sky conditions. The algorithm also captured interannual dynamics and spatial variations in the establishment and disappearance of snow cover. The use of high spatial resolution imagery (LANDSAT or SPOT) would improve the accuracy assessment of the algorithm results according to soil occupation types and pixel fractional snow coverage. The main limitation of the algorithm application is the presence of persistent clouds

Une évaluation de la robustesse de la méthode du krigeage canonique pour l’analyse régionale des débits

Dans le présent article, on se propose d’évaluer la généralité et la robustesse du krigeage canonique, une méthode d’estimation régionale du débit, en l’appliquant pour l’estimation du débit moyen interannuel en régime hydrologique tropical et dans des conditions imparfaites de qualité et de disponibilité de données. Pour ce faire, la méthode du krigeage canonique a été appliquée au cas de Haïti dont le réseau de stations hydrométriques est très limité. Le krigeage canonique a été comparé à la régression linéaire, une méthode simple d’estimation régionale. Selon les critères de performance définis, le krigeage canonique paraît légèrement plus performant que la régression. Il produit des estimations moins biaisées (un biais relatif moyen de ‑ 13 % contre ‑ 20 % pour la régression) et des erreurs relatives légèrement moins importantes (54,4 % contre 59,6 %). Toutefois, le krigeage canonique a été moins performant pour l’estimation du débit des plus grands bassins versants, bien que ses performances globales demeurent acceptables. Par ailleurs, vu les conditions très défavorables dans lesquelles la méthode a été appliquée, il n’a pas été possible de relier la baisse dans les performances du krigeage canonique à une déficience dans la généralité de l’approche et/ou dans sa robustesse.The objective of this study was to test the general application and the robustness of canonical kriging, a new approach regional hydrological estimation. The evaluation of the robustness was carried out for the estimation of mean annual streamflow over the continental territory of Haiti, under a tropical climate and under non-optimal conditions of data quality and availability. The performances of canonical kriging were studied using cross validation. The results were compared to those of the linear regression between the mean annual streamflow and the watershed area applied for the same conditions. In general, canonical kriging yields slightly higher performances. It produces less biased estimates (mean relative bias of ‑ 13% against ‑ 20% for regression) with slightly less significant relative errors (54.4% against 59.6% for regression). However, the linear regression produced better estimates for the largest basins although the global performances of canonical krigeage remained acceptable. In addition, considering the very unfavourable conditions in which the method was applied, it was not possible to connect the decrease in the performances of canonical krigeage to a lack in the general application of the approach and/or its robustness

IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area

A reliable knowledge and assessment of the sea ice conditions and their evolution in time is a priority for numerous decision makers in the domains of coastal and offshore management and engineering as well as in commercial navigation. As of today, countless research projects aimed at both modelling and mapping past, actual and future sea ice conditions were completed using sea ice numerical models, statistical models, educated guesses or remote sensing imagery. From this research, reliable information helping to understand sea ice evolution in space and in time is available to stakeholders. However, no research has, until present, assessed the evolution of sea ice cover with a frequency modelling approach, by identifying the underlying theoretical distribution describing the sea ice behaviour at a given point in space and time. This project suggests the development of a probabilistic tool, named IcePAC, based on frequency modelling of historical 1978–2015 passive microwave sea ice concentrations maps from the EUMETSAT OSI-409 product, to study the sea ice spatio-temporal behaviour in the waters of the Hudson Bay system in northeast Canada. Grid-cell-scale models are based on the generalized beta distribution and generated at a weekly temporal resolution. Results showed coherence with the Canadian Ice Service 1981–2010 Sea Ice Climatic Atlas average freeze-up and melt-out dates for numerous coastal communities in the study area and showed that it is possible to evaluate a range of plausible events, such as the shortest and longest probable ice-free season duration, for any given location in the simulation domain. Results obtained in this project pave the way towards various analyses on sea ice concentration spatio-temporal distribution patterns that would gain in terms of information content and value by relying on the kind of probabilistic information and simulation data available from the IcePAC tool.</p

Seguridad de las aplicaciones web

Conferencia "Seguridad de las aplicaciones web" impartida en la Escuela Politécnica Nacional (Quito, Ecuador) el 22 de mayo de 2014

A Merging Algorithm for Regional Snow Mapping over Eastern Canada from AVHRR and SSM/I Data

We present an algorithm for regional snow mapping that combines snow maps derived from Advanced Very High Resolution Radiometer (AVHRR) and Special Sensor Microwave/Imager (SSM/I) data. This merging algorithm combines AVHRR’s moderate spatial resolution with SSM/I’s ability to penetrate clouds and, thus, benefits from the advantages of the two sensors while minimizing their limitations. First, each of the two detection algorithms were upgraded before developing the methodology to merge the snow mapping results obtained using both algorithms. The merging methodology is based on a membership function calculated over a temporal running window of ±4 days from the actual date. The studied algorithms were developed and tested over Eastern Canada for the period from 1988 to 1999. The snow mapping algorithm focused on the spring melt season (1 April to 31 May). The snow maps were validated using snow depth observations from meteorological stations. The overall accuracy of the merging algorithm is about 86%, which is between that of the new versions of the two individual algorithms: AVHRR (90%) and SSM/I (83%). Furthermore, the algorithm was able to locate the end date of the snowmelt season with reasonable accuracy (bias = 0 days; SD = 11 days). Comparison of mapping results with high spatial resolution snow cover from Landsat imagery demonstrates the feasibility of clear-sky snow mapping with relatively good accuracy despite some underestimation of snow extent inherited from the AVHRR algorithm. It was found that the detection limit of the algorithm is 80% snow cover within a 1 × 1 km pixel

Mapping soil salinity in irrigated land using optical remote sensing data

Soil salinity caused by natural or human-induced processes is certainly a severe environmental problem that already affects 400 million hectares and seriously threatens an equivalent surface. Salinization causes negative effects on the ground; it affects agricultural production, infrastructure, water resources and biodiversity. In semi-arid and arid areas, 21% of irrigated lands suffer from waterlogging, salinity and/or sodicity that reduce their yields. 77 million hectares are saline soils induced by human activity, including 58% in the irrigated areas. In the irrigated perimeter of Tadla plain (central Morocco), the increased use of saline groundwater and surface water, coupled with agricultural intensification leads to the deterioration of soil quality. Experimental methods for monitoring soil salinity by direct measurements in situ are very demanding of time and resources, and also very limited in terms of spatial coverage. Several studies have described the usefulness of remote sensing for mapping salinity by its synoptic coverage and the sensitivity of the electromagnetic signal to surface soil parameters. In this study, we used an image of the TM Landsat sensor and field measurements of electrical conductivity (EC), the correlation between the image data and field measurements allowed us to develop a semi-empirical model allowing the mapping of soil salinity in the irrigated perimeter of Tadla plain. The validation of this model by the ground truth provides a correlation coefficient r² = 0.90. Map obtained from this model allows the identification of different salinization classes in the study area

A Merging Algorithm for Regional Snow Mapping over Eastern Canada from AVHRR and SSM/I Data

We present an algorithm for regional snow mapping that combines snow maps derived from Advanced Very High Resolution Radiometer (AVHRR) and Special Sensor Microwave/Imager (SSM/I) data. This merging algorithm combines AVHRR’s moderate spatial resolution with SSM/I’s ability to penetrate clouds and, thus, benefits from the advantages of the two sensors while minimizing their limitations. First, each of the two detection algorithms were upgraded before developing the methodology to merge the snow mapping results obtained using both algorithms. The merging methodology is based on a membership function calculated over a temporal running window of ±4 days from the actual date. The studied algorithms were developed and tested over Eastern Canada for the period from 1988 to 1999. The snow mapping algorithm focused on the spring melt season (1 April to 31 May). The snow maps were validated using snow depth observations from meteorological stations. The overall accuracy of the merging algorithm is about 86%, which is between that of the new versions of the two individual algorithms: AVHRR (90%) and SSM/I (83%). Furthermore, the algorithm was able to locate the end date of the snowmelt season with reasonable accuracy (bias = 0 days; SD = 11 days). Comparison of mapping results with high spatial resolution snow cover from Landsat imagery demonstrates the feasibility of clear-sky snow mapping with relatively good accuracy despite some underestimation of snow extent inherited from the AVHRR algorithm. It was found that the detection limit of the algorithm is 80% snow cover within a 1 × 1 km pixel

Regionalization of outputs of two crop protection models using geostatistical tools and NOAA-AVHRR images

Crop protection forecasting models currently use meteorological data observed at stations to produce pest infection and development indices. The indices are then extrapolated to the regional level by assuming that the weather conditions at the stations are similar to those in neighbouring fields in the region, which is not necessarily the case. Hence, this has a significant impact on the quality of the recommendations and diagnoses based on computerized plant protection models. The regionalization of model outputs between the stations comprising the weather network, using geostatistical techniques such as cokriging in conjunction with satellite data, is a worthwhile approach for addressing this need. The objective of this study is to develop and apply a methodology for regionalization of infection indices produced by two crop protection models contained in the CIPRA (Computer Centre for Agricultural Pest Forecasting) system, using geostatistical tools and NOAA-AVHRR images. This approach will help enhance our crop pest management and forecasting capabilities while optimizing the use of pest control products in vegetable crops in Quebec. To achieve our objective, a cokriging method was applied to regionalize the model outputs using air temperature and relative humidity estimated from NOAA-AVHRR images. The results were then validated against a regionalization approach using ordinary kriging and two conventional interpolation techniques